HUNG CHAU's blog

Lei Li, Daqing He, Wei Jeng,Spencer Goodwin, Chengzhi Zhang (IW3C2-2015) http://dl.acm.org/citation.cfm?id=2742129 The paper “Answer Quality Characteristics and Prediction on an Academic Q&A Site: A Case Study on ResearchGate” presents a study on ResearchGate, an online social Q&A site. Social Q&A sites are platforms, which allow users (1) interact with others through asking questions and providing answers, and (2) evaluate others’ contributions. Therefore, the quality of user-generated content in such sites are very important; especially, in academic Q&A sites, some questions do not have widely accepted right answers; scholars engage in the discussions to explore knowledge. The answer quality usually is judged in a peer-based fashion; scholars recommend the answer to the question.   Through the study, Lie et al. show the relationship between answer characteristics and answer quality based on peer judgments on ResearchGate, and use useful characteristics as featur

Dr. Vitaly Shmatikov from Cornell Tech gave a talk about "Machine Learning and Privacy: Friends or Foes?" on March, 17, 2017 at School of Information Sciences, University of Pittsburgh. With recent advances in machine learning, there are new powerful tools built on ML models that help to protect data privacy. However, may trained models result in leaking sensitive data? In this talk, Dr Shmatikov presented two part: (1) How to use machine learning against systems by partially encrypt user data in storage (e.g., images); (2) How to turn machine learning against itself, to extract sensitive training data from machine learning models,  including black-box models constructed using Google's and Amazon's "learning-as-a-service" platforms. At the beginning of the talk, Dr. Shmatikov introduced a quick overview about machine learning, how it works and outperforms humans. Advanced machine learning is able to deal with typical tasks such as image classification b

Professor Christos Faloutsos from Carnegie Mellon University held a talk on "Anomaly Detection in Large Graphs" on February, 24, 2017 at University of Pittsburgh. Given a large graph such as who-follows-whom, who-calls-whom, or who-likes-whom, observing some patterns in the graph can we tell what is normal behaviors and what is abnormal behaviors, which probably are resulted from fraudulent activities? And how graph evolves over time? Prof. Faloutsos presented two parts: (1) how to mine patterns and how to detect fraud in a static graph, and (2) patterns and anomalies in large time-evolving graphs. In the first part, Prof. Faloutsos claimed that real graphs are not random, for example, in- and out- degree distributions. He gave a list of laws and patterns of graphs, including: The power law in the degree distribution (connected component sizes): we can find patterns about the degree of graph. Singular values and eigen values: also used to find patterns about degree dist

Suzan Verberne, Maya Sappelli, Wessel Kraaij ( UMAP-2013 ) http://sverberne.ruhosting.nl/papers/swell_umap_cameraready.pdf There are several methods that social information systems use to recommend people to their users. Among these methods, content-based people recommendation is most widely used in many different systems (e.g., Twitter). The systems collect information from users such as publications, blogs, microblogs, or posts to build their profiles in order to do recommendation later. How to build effective user profiles from texts is a difficult task. Most of the studies represent texts as a bag-of-word. Some other try to extract more meaningful terms or phrases. If a system has a good representation for user profile, it will be able to find more accurately similar users so that it can generate good people recommendation to each user. In this paper, Verberne et al. present a study that compares three popular methods of weighting terms for user profiling. The results of this

Jing Peng, Daniel Zeng,  Huimin Zhao, Fei-yue Wang ( CIKM-10 ) http://dl.acm.org/citation.cfm?id=1871541 The paper “Collaborative Filtering in Social Tagging Systems Based on Joint Item-Tag Recommendations” introduces a novel framework for collaborative filtering in social tagging systems. In recent years, social tagging has been gaining wide-spread popularity in a variety of applications. Enabling automated recommendation of various kinds in social tagging systems can further enhance this important social information discovery mechanism. However, all of the previous research focuses on recommendations of either items or tags. If we can leverage tag information and integrate it into our system, it could help to improve the performance of recommendation engine. Firstly, Peng et al. present a structure that integrates all possible co-occurrence information among the three entities (i.e., User, Item, and Tag) into one framework. Different with other work that usually consider only